Huang S.,Institute for Systems Biology
Progress in Biophysics and Molecular Biology | Year: 2012
Current investigation of cancer progression towards increasing malignancy focuses on the molecular pathways that produce the various cancerous traits of cells. Their acquisition is explained by the somatic mutation theory: tumor progression is the result of a neo-Darwinian evolution in the tissue. Herein cells are the units of selection. Random genetic mutations permanently affecting these pathways create malignant cell phenotypes that are selected for in the disturbed tissue. However, could it be that the capacity of the genome and its gene regulatory network to generate the vast diversity of cell types during development, i.e., to produce inheritable phenotypic changes without mutations, is harnessed by tumorigenesis to propel a directional change towards malignancy? Here we take an encompassing perspective, transcending the orthodoxy of molecular carcinogenesis and review mechanisms of somatic evolution beyond the Neo-Darwinian scheme. We discuss the central concept of " cancer attractors" - the hidden stable states of gene regulatory networks normally not occupied by cells. Noise-induced transitions into such attractors provide a source for randomness (chance) and regulatory constraints (necessity) in the acquisition of novel expression profiles that can be inherited across cell divisions, and hence, can be selected for. But attractors can also be reached in response to environmental signals - thus offering the possibility for inheriting acquired traits that can also be selected for. Therefore, we face the possibility of non-genetic (mutation-independent) equivalents to both Darwinian and Lamarckian evolution which may jointly explain the arrow of change pointing toward increasing malignancy. © 2012 Elsevier Ltd.
Deutsch E.W.,Institute for Systems Biology
Molecular and Cellular Proteomics | Year: 2012
The application of mass spectrometry (MS) to the analysis of proteomes has enabled the high-throughput identification and abundance measurement of hundreds to thousands of proteins per experiment. However, the formidable informatics challenge associated with analyzing MS data has required a wide variety of data file formats to encode the complex data types associated with MS workflows. These formats encompass the encoding of input instruction for instruments, output products of the instruments, and several levels of information and results used by and produced by the informatics analysis tools. A brief overview of the most common file formats in use today is presented here, along with a discussion of related topics. © 2012 by The American Society for Biochemistry and Molecular Biology Inc.
Huang S.,Institute for Systems Biology
BioEssays | Year: 2012
The Neo-Darwinian concept of natural selection is plausible when one assumes a straightforward causation of phenotype by genotype. However, such simple 1:1 mapping must now give place to the modern concepts of gene regulatory networks and gene expression noise. Both can, in the absence of genetic mutations, jointly generate a diversity of inheritable randomly occupied phenotypic states that could also serve as a substrate for natural selection. This form of epigenetic dynamics challenges Neo-Darwinism. It needs to incorporate the non-linear, stochastic dynamics of gene networks. A first step is to consider the mathematical correspondence between gene regulatory networks and Waddington's metaphoric 'epigenetic landscape', which actually represents the quasi-potential function of global network dynamics. It explains the coexistence of multiple stable phenotypes within one genotype. The landscape's topography with its attractors is shaped by evolution through mutational re-wiring of regulatory interactions - offering a link between genetic mutation and sudden, broad evolutionary changes. © 2012 WILEY Periodicals, Inc.
Shteynberg D.,Institute for Systems Biology
Molecular & cellular proteomics : MCP | Year: 2011
The combination of tandem mass spectrometry and sequence database searching is the method of choice for the identification of peptides and the mapping of proteomes. Over the last several years, the volume of data generated in proteomic studies has increased dramatically, which challenges the computational approaches previously developed for these data. Furthermore, a multitude of search engines have been developed that identify different, overlapping subsets of the sample peptides from a particular set of tandem mass spectrometry spectra. We present iProphet, the new addition to the widely used open-source suite of proteomic data analysis tools Trans-Proteomics Pipeline. Applied in tandem with PeptideProphet, it provides more accurate representation of the multilevel nature of shotgun proteomic data. iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. The use of iProphet in the Trans-Proteomics Pipeline increases the number of correctly identified peptides at a constant false discovery rate as compared with both PeptideProphet and another state-of-the-art tool Percolator. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and false discovery rate estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. Fully integrated with the Trans-Proteomics Pipeline, it supports all commonly used MS instruments, search engines, and computer platforms. The performance of iProphet is demonstrated on two publicly available data sets: data from a human whole cell lysate proteome profiling experiment representative of typical proteomic data sets, and from a set of Streptococcus pyogenes experiments more representative of organism-specific composite data sets.
Huang S.,Institute for Systems Biology
PLoS Biology | Year: 2013
Polarization of cell phenotypes, a common strategy to achieve cell type diversity in metazoa, results from binary cell-fate decisions in the branching pedigree of development. Such "either-or" fate decisions are controlled by two opposing cell fate-determining transcription factors. Each of the two distinct "master regulators" promotes differentiation of its respective sister lineage. But they also suppress one other, leading to their mutually exclusive expression in the two ensuing lineages. Thus, promiscuous coexistence of the antagonist regulators in the same cell, the hallmark of the common "undecided" progenitor of two sister lineages, is considered unstable. This antagonism ensures robust polarization into two discretely distinct cell types. But now the immune system's T-helper (Th) cells and their two canonical subtypes, Th1 and Th2 cells, tell a different story, as revealed in three papers recently published in PLOS Biology. The intermediate state that co-expresses the two opposing master regulators of the Th1 and Th2 subtypes, T-bet and Gata3, is highly stable and is not necessarily an undecided precursor. Instead, the Th1/Th2 hybrid cell is a robust new type with properties of both Th1 and Th2 cells. These hybrid cells are functionally active and possess the benefit of moderation: self-limitation of effector T cell function to prevent excessive inflammation, a permanent risk in host defense that can cause tissue damage or autoimmunity. Gene regulatory network analysis suggests that stabilization of the intermediate center in a polarizing system can be achieved by minor tweaking of the architecture of the mutual suppression gene circuit, and thus is a design option readily available to evolution. © 2013 Huang.